code: def test_splits_fitted(cluster_method, random_xy_dataset_regr):
      cluster_method = clone(cluster_method)
      X, y = random_xy_dataset_regr
      cluster_method = cluster_method.fit(X)
      kf = KlusterFoldValidation(cluster_method=cluster_method)
      for train_index, test_index in kf.split(X):
          assert len(train_index) > 0
          assert len(test_index) > 0
  
file_path: scikit-lego/tests/test_model_selection/test_klusterfold.py
imports: import numpy
  import pytest
  from sklearn import clone
  from sklearn.base import BaseEstimator
  from sklearn.cluster import KMeans, MiniBatchKMeans
  from sklearn.pipeline import make_pipeline
  from sklearn.preprocessing import StandardScaler
  from sklego.model_selection import KlusterFoldValidation
  from tests.conftest import id_func
label_desc: Use image node_id 2 for calling a global function with example usage: test_splits_fitted(cluster_method, random_xy_dataset_regr) without return types